Results 181 to 190 of about 7,871,149 (237)

Generalized Estimating Equations

Technometrics, 2003
E. C. Chao
semanticscholar   +3 more sources

M-Estimation (Estimating Equations)

2012
In Chapter 1 we made the distinction between the parts of a fully specified statistical model. The primary part is the part that is most important for answering the underlying scientific questions. The secondary part consists of all the remaining details of the model.
Denni D Boos, L A Stefanski
openaire   +1 more source

Model Selection in Estimating Equations

Biometrics, 2001
Summary.Model selection is a necessary step in many practical regression analyses. But for methods based on estimating equations, such as the quasi‐likelihood and generalized estimating equation (GEE) approaches, there seem to be few well‐studied model selection techniques.
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Generalized Estimating Equation

2017
The generalized estimating equation (GEE) uses a quasi-likelihood approach for analyzing data with correlated outcomes. This is an extension of GLM and uses quasi-likelihood method for cluster or repeated outcomes. If observations on outcome variable are repeated, it is likely that the observations are correlated.
M. Ataharul Islam, Rafiqul I. Chowdhury
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Generalized Estimating Equations

Journal of the American Statistical Association, 2004
(2004). Generalized Estimating Equations. Journal of the American Statistical Association: Vol. 99, No. 465, pp. 297-298.
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Local Estimating Equations

Journal of the American Statistical Association, 1998
Abstract Estimating equations have found wide popularity recently in parametric problems, yielding consistent estimators with asymptotically valid inferences obtained via the sandwich formula. Motivated by a problem in nutritional epidemiology, we use estimating equations to derive nonparametric estimators of a “parameter” depending on a predictor. The
Raymond J. Carroll, David Ruppert, Alan H. Welsh   +2 more
openaire   +1 more source

Sequential Estimation through Estimating Equations

1994
Using the approach to estimation through estimating equations, the information inequalities for regular sequential estimating plans are given and an optimum property of regular maximum likelihood sequential plans is shown in a general model for stochastic processes. The optimum sequential estimating functions are obtained in the nuisance parameter case
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Multiparametric estimating equations

Annals of the Institute of Statistical Mathematics, 1982
Let {p(x, θ): θ∈Θ} be a family of densities where θ=(θ1,θ2), being θ1 ∈ Θ1 ak-dimensional parameter of interest, θ2 ∈ Θ2 a nuisance parameter and Θ=Θ1×Θ2. To estimate θ1, vector estimating equations g(x,θ1)=(g1(x,θ1),...,gk(x,θ1))=0 are considered. The standardized form of g(x,θ1) is defined as gs=(Eθ(∂g/∂θ′1))−1g. Then, within the classG 1 of unbiased
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Generalised Estimation Equations

2009
In this chapter, we analyse three data sets; California birds, owls, and deer. In the first data set, the response variable is the number of birds measured repeatedly over time at two-weekly intervals at the same locations. In the owl data set (Chapter 5), the response variable is the number of calls made by all offspring in the absence of the parent ...
Alain F. Zuur, Elena N. Ieno, Neil J. Walker, Anatoly A. Saveliev, Graham M. Smith   +4 more
openaire   +1 more source

Akaike's Information Criterion in Generalized Estimating Equations

Biometrics, 2001
W. Pan
semanticscholar   +1 more source